# Practice with R
## Creating vectors/factors and dataframes
6. We are performing RNA-Seq on cancer samples being treated with three different types of treatment (A, B, and P). You have 12 samples total, with 4 replicates per treatment. Write the R code you would use to construct your metadata table as described below.
- Create the vectors/factors for each column.
- Put them together into a dataframe called `meta`. (Hint: you can type out each vector/factor, or if you want the process go faster try exploring the rep() function).
- Use the row.names() function to assign row names to the dataframe.
Your finished metadata table should have information for the variables "sex", "stage", "treatment", and "myc" levels:
| sex | stage | treatment | myc | |
|---|---|---|---|---|
| sample1 | M | I | A | 2343 |
| sample2 | F | II | A | 457 |
| sample3 | M | II | A | 4593 |
| sample4 | F | I | A | 9035 |
| sample5 | M | II | B | 3450 |
| sample6 | F | II | B | 3524 |
| sample7 | M | I | B | 958 |
| sample8 | F | II | B | 1053 |
| sample9 | M | II | P | 8674 |
| sample10 | F | I | P | 3424 |
| sample11 | M | II | P | 463 |
| sample12 | F | II | P | 5105 |
## Subsetting vectors/factors and dataframes
7. Using the metadata table from question #6, write out the R code you would use to perform the following operations (questions DO NOT build upon each other):
a. return only the "treatment" and "sex" columns:
b. return the "treatment" values for samples 5, 7, 9, and 10:
c. use subset() to return all data for those samples receiving treatment "P":
d. use subset() to return only the "stage" and "treatment" data for those samples with "myc" > 5000:
e. remove the "treatment" column from the dataset:
f. remove samples 7, 8 and 9 from the dataset:
g. keep only samples 1-6:
h. add a column called "pre_treatment" to the beginning of the dataframe with the values T, F, F, F, T, T, F, T, F, F, T, T (Hint: use cbind()):
i. change the names of the columns to: "A", "B", "C", "D":
## Lists
8. Create a new list, "list2" with three components, the "glengths" vector, the dataframe "df", and "number" value. Use this list to answer the questions below . "list2" has the following structure (NOTE: the components of this list are not currently named):
[[1]]
[1] 4.6 3000.0 50000.0
[[2]]
species glengths
1 ecoli 4.6
2 human 3000.0
3 corn 50000.0
[[3]]
[1] 8
Write out the R code you would use to perform the following operations (questions DO NOT build upon each other):
a. return the second component of the list:
b. return "50000.0" from the first component of the list:
c. return the value "human" from the second component:
d. give the components of the list the following names: "genome_lengths", "genomes", "record". Then return "record":
## Nested functions
9. Let's derive some nested functions similar to those we will use in our RNA-Seq analysis. The dataframes, value_table and meta, should be used to address the questions below:
value_table
| MX1 | MX2 | MX3 | |
|---|---|---|---|
| KD.2 | -222517.197 | -21756.82 | -16036.035 |
| KD.3 | 17453.907 | -30058.14 | -25837.482 |
| OE.1 | -31247.923 | 73061.38 | 7019.940 |
| OE.2 | -4184.355 | 61994.47 | 1777.858 |
| OE.3 | 147391.709 | 11970.45 | -18663.686 |
| IR.1 | -32247.617 | -27896.01 | 29383.153 |
| IR.2 | 25456.820 | -30714.29 | 19148.752 |
| IR.3 | 99894.656 | -36601.04 | 3207.501 |
meta
| sampletype | MOVexpr | |
|---|---|---|
| KD.2 | MOV10_knockdown | low |
| KD.3 | MOV10_knockdown | low |
| OE.1 | MOV10_overexpression | high |
| OE.2 | MOV10_overexpression | high |
| OE.3 | MOV10_overexpression | high |
| IR.1 | siRNA | normal |
| IR.2 | siRNA | normal |
| IR.3 | siRNA | normal |
- We would like to count the number of samples which have normal Mov10 expression in the meta dataset. Let's do this in steps:
- Write the R code you would run to provide the location of samples with MOVexpr equal to "normal":
- Write the R code you would run to determine the number of elements in the MOVexpr column:
- Now, try to combine your first two actions into a single line of code using nested functions to determine the number of elements in the MOVexpr column with expression levels of MOV10 being normal:
- Write the R code you would run to provide the location of samples with MOVexpr equal to "normal":
- We would like to add the MX1 and MX3 columns of the value_table to our meta file. Let's do this in steps:
- Write the R code you would run to extract columns MX1 and MX3 from the value_table and to save it to a variable 'mx' (hint: you will need to use the c() function to specify the columns you want to extract):
- Using the cbind function, write the R code you would use to add the columns in your 'mx' variable to the end of your meta dataset :
- Now, try to combine your first two actions into a single line of code using nested functions (hint: you do not need to generate the 'mx' variable) to add the MX1 and MX3 columns to the meta file:
- Write the R code you would run to extract columns MX1 and MX3 from the value_table and to save it to a variable 'mx' (hint: you will need to use the c() function to specify the columns you want to extract):
- Finally, we would like to extract only those rows from the meta dataset for replicate 2 from all conditions (KD.2, OE.2, IR.2). Let's do this in steps:
- Write the function you would use to determine the row names of the meta dataset:
- Using the which() function, write the R code you would run to determine the location of the row name KD.2 in the meta dataset:
- Using the which() function, write the R code you would use to determine the location of row names KD.2, OE.2, and IR.2 in the meta dataset (use the OR operator ( | ) to return multiple locations):
- Now, extract the rows from the meta dataset with row names KD.2, OE.2, and IR.2 using a single line of code using nested functions:
- Write the function you would use to determine the row names of the meta dataset: